OpenRaiser/Intern-Atlas huggingface dataset:
NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe
Venue: arXiv cs.SD 2025
Year: 2025

The persisting threats on migratory bird populations highlight the urgent need for effective monitoring techniques that could assist in their conservation. Among these, passive acoustic monitoring is an essential tool, particularly for nocturnal migratory species that are difficult to track otherwise. This work presents the Nocturnal Bird Migration (NBM) dataset, a collection of 13,359 annotated vocalizations from 117 species of the Western Palearctic. The dataset includes precise time and frequency annotations, gathered by dozens of bird enthusiasts across France, enabling novel downstream acoustic analysis. In particular, we prove the utility of this database by training an original two-stage deep object detection model tailored for the processing of audio data. While allowing the precise localization of bird calls in spectrograms, this model shows competitive accuracy on the 45 main species of the dataset with state-of-the-art systems trained on much larger audio collections. These results highlight the interest of fostering similar open-science initiatives to acquire costly but valuable fine-grained annotations of audio files. All data and code are made openly available.
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
Venue: arXiv cs.LG 2025
Year: 2025

Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most research has centered on static graphs, many real-world scenarios involve dynamic, temporally evolving graphs, motivating the need for Continuous-Time Dynamic Graph (CTDG) models. This paper provides a comprehensive review of Graph Representation Learning (GRL) on CTDGs with a focus on Self-Supervised Representation Learning (SSRL). We introduce a novel theoretical framework that analyzes the expressivity of CTDG models through an Information-Flow (IF) lens, quantifying their ability to propagate and encode temporal and structural information. Leveraging this framework, we categorize existing CTDG methods based on their suitability for different graph types and application scenarios. Within the same scope, we examine the design of SSRL methods tailored to CTDGs, such as predictive and contrastive approaches, highlighting their potential to mitigate the reliance on labeled data. Empirical evaluations on synthetic and real-world datasets validate our theoretical insights, demonstrating the strengths and limitations of various methods across long-range, bi-partite and community-based graphs. This work offers both a theoretical foundation and practical guidance for selecting and developing CTDG models, advancing the understanding of GRL in dynamic settings.
Dudeney's Dissection is Optimal
Venue: arXiv cs.CG 2025
Year: 2025

In 1907, Henry Ernest Dudeney posed a puzzle: ``cut any equilateral triangle \dots\ into as few pieces as possible that will fit together and form a perfect square'' (without overlap, via translation and rotation). Four weeks later, Dudeney demonstrated a beautiful four-piece solution, which today remains perhaps the most famous example of dissection. In this paper (over a century later), we finally solve Dudeney's puzzle, by proving that the equilateral triangle and square have no common dissection with three or fewer polygonal pieces. We reduce the problem to the analysis of discrete graph structures representing the correspondence between the edges and the vertices of the pieces forming each polygon.
Learning to Hash for Recommendation: A Survey
Venue: arXiv cs.IR 2025
Year: 2025

With the explosive growth of users and items, Recommender Systems are facing unprecedented challenges in terms of retrieval efficiency and storage overhead. Learning to Hash techniques have emerged as a promising solution to these issues by encoding high-dimensional data into compact hash codes. As a result, hashing-based recommendation methods (HashRec) have garnered growing attention for enabling large-scale and efficient recommendation services. This survey provides a comprehensive overview of state-of-the-art HashRec algorithms. Specifically, we begin by introducing the common two-tower architecture used in the recall stage and by detailing two predominant hash search strategies. Then, we categorize existing works into a three-tier taxonomy based on: (i) learning objectives, (ii) optimization strategies, and (iii) recommendation scenarios. Additionally, we summarize widely adopted evaluation metrics for assessing both the effectiveness and efficiency of HashRec algorithms. Finally, we discuss current limitations in the field and outline promising directions for future research. We index these HashRec methods at the repository \href{https://github.com/Luo-Fangyuan/HashRec}{https://github.com/Luo-Fangyuan/HashRec}.
CRAFT: Designing Creative and Functional 3D Objects
Venue: arXiv cs.CV 2025
Year: 2025

For designing a wide range of everyday objects, the design process should be aware of both the human body and the underlying semantics of the design specification. However, these two objectives present significant challenges to the current AI-based designing tools. In this work, we present a method to synthesize body-aware 3D objects from a base mesh given an input body geometry and either text or image as guidance. The generated objects can be simulated on virtual characters, or fabricated for real-world use. We propose to use a mesh deformation procedure that optimizes for both semantic alignment as well as contact and penetration losses. Using our method, users can generate both virtual or real-world objects from text, image, or sketch, without the need for manual artist intervention. We present both qualitative and quantitative results on various object categories, demonstrating the effectiveness of our approach.
Dimension Reduction via Random Projection for Privacy in Multi-Agent Systems
Venue: arXiv cs.CR 2025
Year: 2025

In a Multi-Agent System (MAS), individual agents observe various aspects of the environment and transmit this information to a central entity responsible for aggregating the data and deducing system parameters. To improve overall efficiency, agents may append certain private parameters to their observations. For example, in a crowd-sourced traffic monitoring system, commuters might share not only their current speed, but also sensitive information such as their location to enable more accurate route prediction. However, sharing such data can allow the central entity or a potential adversary to infer private details about the user, such as their daily routines. To mitigate these privacy risks, the agents sanitize the data before transmission. This sanitization inevitably results in a loss of utility. In this work, we formulate the problem as a utility-privacy trade-off and propose a novel compression-based approach leveraging the notion of robust concepts to sanitize the shared data. We further derive a bound on the norm of the compression matrix required to ensure maximal privacy while satisfying predefined utility constraints.
Learning Dual-Arm Push and Grasp Synergy in Dense Clutter
Venue: arXiv cs.RO 2025
Year: 2025

Robotic grasping in densely cluttered environments is challenging due to scarce collision-free grasp affordances. Non-prehensile actions can increase feasible grasps in cluttered environments, but most research focuses on single-arm rather than dual-arm manipulation. Policies from single-arm systems fail to fully leverage the advantages of dual-arm coordination. We propose a target-oriented hierarchical deep reinforcement learning (DRL) framework that learns dual-arm push-grasp synergy for grasping objects to enhance dexterous manipulation in dense clutter. Our framework maps visual observations to actions via a pre-trained deep learning backbone and a novel CNN-based DRL model, trained with Proximal Policy Optimization (PPO), to develop a dual-arm push-grasp strategy. The backbone enhances feature mapping in densely cluttered environments. A novel fuzzy-based reward function is introduced to accelerate efficient strategy learning. Our system is developed and trained in Isaac Gym and then tested in simulations and on a real robot. Experimental results show that our framework effectively maps visual data to dual push-grasp motions, enabling the dual-arm system to grasp target objects in complex environments. Compared to other methods, our approach generates 6-DoF grasp candidates and enables dual-arm push actions, mimicking human behavior. Results show that our method efficiently completes tasks in densely cluttered environments. https://sites.google.com/view/pg4da/home
Expand Heterogeneous Learning Systems with Selective Multi-Source Knowledge Fusion
Venue: arXiv cs.AI 2025
Year: 2025

Expanding existing learning systems to provide high-quality customized models for more domains, such as new users, is challenged by the limited labeled data and the data and device heterogeneities. While knowledge distillation methods could overcome label scarcity and device heterogeneity, they assume the teachers are fully reliable and overlook the data heterogeneity, which prevents the direct adoption of existing models. To address this problem, this paper proposes a framework, HaT, to expand learning systems. It first selects multiple high-quality models from the system at a low cost and then fuses their knowledge by assigning sample-wise weights to their predictions. Later, the fused knowledge is selectively injected into the customized models based on the knowledge quality. Extensive experiments on different tasks, modalities, and settings show that HaT outperforms state-of-the-art baselines by up to 16.5% accuracy and saves up to 39% communication traffic.
Recursively Extended Permutation Codes under Chebyshev Distance
Venue: arXiv cs.IT 2025
Year: 2025

This paper investigates the construction and analysis of permutation codes under the Chebyshev distance. Direct product group permutation (DPGP) codes, independently introduced by Kløve et al. and Tamo et al., represent the best-known class of permutation codes in terms of both size and minimum distance, while also allowing for algebraic and efficient encoding and decoding. In contrast, this study focuses on recursively extended permutation (REP) codes, proposed by Kløve et al. as a recursive alternative. We analyze the properties of REP codes and prove that, despite their distinct construction principles, optimal REP codes achieve exactly the same size and minimum distance as the best DPGP codes under the Chebyshev metric. This surprising equivalence uncovers a deep connection between two structurally dissimilar code families and establishes REP codes as a structurally flexible yet equally powerful alternative to DPGP codes. In addition, we present efficient encoding and decoding algorithms for REP codes, including a sequential encoder with $O(n \log n)$ complexity and a bounded-distance decoder with $O(n \log^2 n)$ complexity.
HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing
Venue: arXiv cs.CV 2025
Year: 2025

We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and diverse image manipulations through open-form language instructions. Previous large-scale editing datasets often incorporate minimal human feedback, leading to challenges in aligning datasets with human preferences. HumanEdit bridges this gap by employing human annotators to construct data pairs and administrators to provide feedback. With meticulously curation, HumanEdit comprises 5,751 images and requires more than 2,500 hours of human effort across four stages, ensuring both accuracy and reliability for a wide range of image editing tasks. The dataset includes six distinct types of editing instructions: Action, Add, Counting, Relation, Remove, and Replace, encompassing a broad spectrum of real-world scenarios. All images in the dataset are accompanied by masks, and for a subset of the data, we ensure that the instructions are sufficiently detailed to support mask-free editing. Furthermore, HumanEdit offers comprehensive diversity and high-resolution $1024 \times 1024$ content sourced from various domains, setting a new versatile benchmark for instructional image editing datasets. With the aim of advancing future research and establishing evaluation benchmarks in the field of image editing, we release HumanEdit at https://huggingface.co/datasets/BryanW/HumanEdit.
Retrieval-Augmented Machine Translation with Unstructured Knowledge
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-augmented generation (RAG) introduces additional information to enhance large language models (LLMs). In machine translation (MT), previous work typically retrieves in-context examples from paired MT corpora, or domain-specific knowledge from knowledge graphs, to enhance MT models. However, a large amount of world knowledge is organized in unstructured documents, and might not be fully paired across different languages. In this paper, we study retrieval-augmented MT using unstructured documents. Specifically, we build RAGtrans, the first benchmark to train and evaluate LLMs' retrieval-augmented MT ability. RAGtrans contains 169K MT samples collected via GPT-4o and human translators. Besides, documents from various languages are also provided to supply the knowledge to these samples. Based on RAGtrans, we further propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation. The method uses existing multilingual corpora to create auxiliary training objectives without additional labeling requirements. Extensive experiments show that the method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLEU and 2.1-2.7 COMET scores in En-De. We also conclude the critical difficulties that current LLMs face with this task.
Distributionally Robust Performative Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Performative prediction aims to model scenarios where predictive outcomes subsequently influence the very systems they target. The pursuit of a performative optimum (PO) -- minimizing performative risk -- is generally reliant on modeling of the distribution map, which characterizes how a deployed ML model alters the data distribution. Unfortunately, inevitable misspecification of the distribution map can lead to a poor approximation of the true PO. To address this issue, we introduce a novel framework of distributionally robust performative prediction and study a new solution concept termed as distributionally robust performative optimum (DRPO). We show provable guarantees for DRPO as a robust approximation to the true PO when the nominal distribution map is different from the actual one. Moreover, distributionally robust performative prediction can be reformulated as an augmented performative prediction problem, enabling efficient optimization. The experimental results demonstrate that DRPO offers potential advantages over traditional PO approach when the distribution map is misspecified at either micro- or macro-level.
Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules
Venue: arXiv cs.CV 2025
Year: 2025

Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis. In particular, there is interest in enabling view synthesis for dynamic scenes using only monocular input data -- an ill-posed and challenging problem. The fast pace of work in this area has produced multiple simultaneous papers that claim to work best, which cannot all be true. In this work, we organize, benchmark, and analyze many Gaussian-splatting-based methods, providing apples-to-apples comparisons that prior works have lacked. We use multiple existing datasets and a new instructive synthetic dataset designed to isolate factors that affect reconstruction quality. We systematically categorize Gaussian splatting methods into specific motion representation types and quantify how their differences impact performance. Empirically, we find that their rank order is well-defined in synthetic data, but the complexity of real-world data currently overwhelms the differences. Furthermore, the fast rendering speed of all Gaussian-based methods comes at the cost of brittleness in optimization. We summarize our experiments into a list of findings that can help to further progress in this lively problem setting.
PaintScene4D: Consistent 4D Scene Generation from Text Prompts
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in diffusion models have revolutionized 2D and 3D content creation, yet generating photorealistic dynamic 4D scenes remains a significant challenge. Existing dynamic 4D generation methods typically rely on distilling knowledge from pre-trained 3D generative models, often fine-tuned on synthetic object datasets. Consequently, the resulting scenes tend to be object-centric and lack photorealism. While text-to-video models can generate more realistic scenes with motion, they often struggle with spatial understanding and provide limited control over camera viewpoints during rendering. To address these limitations, we present PaintScene4D, a novel text-to-4D scene generation framework that departs from conventional multi-view generative models in favor of a streamlined architecture that harnesses video generative models trained on diverse real-world datasets. Our method first generates a reference video using a video generation model, and then employs a strategic camera array selection for rendering. We apply a progressive warping and inpainting technique to ensure both spatial and temporal consistency across multiple viewpoints. Finally, we optimize multi-view images using a dynamic renderer, enabling flexible camera control based on user preferences. Adopting a training-free architecture, our PaintScene4D efficiently produces realistic 4D scenes that can be viewed from arbitrary trajectories. The code will be made publicly available. Our project page is at https://paintscene4d.github.io/
Communication Compression for Distributed Learning without Control Variates
Venue: arXiv cs.LG 2025
Year: 2025

Distributed learning algorithms, such as the ones employed in Federated Learning (FL), require communication compression to reduce the cost of client uploads. The compression methods used in practice are often biased, making error feedback necessary both to achieve convergence under aggressive compression and to provide theoretical convergence guarantees. However, error feedback requires client-specific control variates, creating two key challenges: it violates privacy-preserving principles and demands stateful clients. In this paper, we propose Compressed Aggregate Feedback (CAFe), a novel distributed learning framework that allows highly compressible client updates by exploiting past aggregated updates, and does not require control variates. We consider Distributed Gradient Descent (DGD) as a representative algorithm and analytically prove CAFe's superiority to Distributed Compressed Gradient Descent (DCGD) with biased compression in the non-convex regime with bounded gradient dissimilarity. Experimental results confirm that CAFe outperforms existing distributed learning compression schemes.
Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Timely and accurate assessments of building damage are crucial for effective response and recovery in the aftermath of earthquakes. Conventional preliminary damage assessments (PDA) often rely on manual door-to-door inspections, which are not only time-consuming but also pose significant safety risks. To safely expedite the PDA process, researchers have studied the applicability of satellite imagery processed with heuristic and machine learning approaches. These approaches output binary or, more recently, multiclass damage states at the scale of a block or a single building. However, the current performance of such approaches limits practical applicability. To address this limitation, we introduce a metadata-enriched, transformer based framework that combines high-resolution post-earthquake satellite imagery with building-specific metadata relevant to the seismic performance of the structure. Our model achieves state-of-the-art performance in multiclass post-earthquake damage identification for buildings from the Turkey-Syria earthquake on February 6, 2023. Specifically, we demonstrate that incorporating metadata, such as seismic intensity indicators, soil properties, and SAR damage proxy maps not only enhances the model's accuracy and ability to distinguish between damage classes, but also improves its generalizability across various regions. Furthermore, we conducted a detailed, class-wise analysis of feature importance to understand the model's decision-making across different levels of building damage. This analysis reveals how individual metadata features uniquely contribute to predictions for each damage class. By leveraging both satellite imagery and metadata, our proposed framework enables faster and more accurate damage assessments for precise, multiclass, building-level evaluations that can improve disaster response and accelerate recovery efforts for affected communities.
Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens
Venue: arXiv cs.CV 2025
Year: 2025

Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success lies in the attention mechanism, which models relationships among tokens. While the tokenization process in NLP inherently ensures that a single token does not contain multiple semantics, the tokenization of Vision Transformer (ViT) utilizes tokens from uniformly partitioned square image patches, which may result in an arbitrary mixing of visual concepts in a token. In this work, we propose to substitute the grid-based tokenization in ViT with superpixel tokenization, which employs superpixels to generate a token that encapsulates a sole visual concept. Unfortunately, the diverse shapes, sizes, and locations of superpixels make integrating superpixels into ViT tokenization rather challenging. Our tokenization pipeline, comprised of pre-aggregate extraction and superpixel-aware aggregation, overcomes the challenges that arise in superpixel tokenization. Extensive experiments demonstrate that our approach, which exhibits strong compatibility with existing frameworks, enhances the accuracy and robustness of ViT on various downstream tasks.
ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

The increasing frequency and sophistication of cybersecurity vulnerabilities in software systems underscores the need for more robust and effective vulnerability assessment methods. However, existing approaches often rely on highly technical and abstract frameworks, which hinder understanding and increase the likelihood of exploitation, resulting in severe cyberattacks. In this paper, we introduce ChatNVD, a support tool powered by Large Language Models (LLMs) that leverages the National Vulnerability Database (NVD) to generate accessible, context-rich summaries of software vulnerabilities. We develop three variants of ChatNVD, utilizing three prominent LLMs: GPT-4o Mini by OpenAI, LLaMA 3 by Meta, and Gemini 1.5 Pro by Google. To evaluate their performance, we conduct a comparative evaluation focused on their ability to identify, interpret, and explain software vulnerabilities. Our results demonstrate that GPT-4o Mini outperforms the other models, achieving over 92% accuracy and the lowest error rates, making it the most reliable option for real-world vulnerability assessment.